Hume-Rothery Symposium on First-Principles Materials Design: Interface First-principle Method with the Discovery of Energy Materials
Sponsored by: TMS Functional Materials Division, TMS Structural Materials Division, TMS: Alloy Phases Committee
Program Organizers: Bin Ouyang, Florida State University; Mark Asta, University of California, Berkeley; Geoffroy Hautier, Dartmouth College; Wei Xiong, University of Pittsburgh; Anton Van der Ven, University of California, Santa Barbara
Monday 2:00 PM
March 20, 2023
Room: Cobalt 501C
Location: Hilton
Session Chair: Pieremanuele Canepa, National University of Singapore; Bin Ouyang, Florida State University
2:00 PM Invited
Disorder and Degradation in Rock-salt-type Lithium-ion Battery Cathodes: Alexander Urban1; 1Columbia University
Currently, the most energy-dense lithium-ion batteries are based on rock-salt-type cathodes, and improving on the prior art without sacrificing lifetime has been a formidable challenge. Here I will review our current understanding of structure-composition-stability/capacity relationships in rock-salt-type electrodes, obtained from first-principles calculations, percolation theory, model Hamiltonian analysis, computational phase diagrams, and machine learning. In layered oxides with the general composition LiMO2 (M = one or more metal species) in which lithium ions are segregated into separate layers, Li/M disorder leads to the loss of reversible capacity. In contrast, in Li-rich compositions Li1+xM1-xO2, Li/M disorder can be advantageous. A common mode of degradation in both layered and disordered oxides is the release of oxygen from the surface of the electrode at high voltages or high temperatures, which coincides with the formation of surface reconstructions. I will discuss similarities/differences of ordered and disordered cathodes and point out open questions.
2:30 PM Invited
First Principle Design of High Entropy Materials for Energy Storage and Conversion: Bin Ouyang1; Gerbrand Ceder2; 1Florida State University; 2University of California Berkeley
High entropy materials promised a lot of opportunities in the field of structural metal alloys due to their extraordinary mechanical properties. It is also rising up in many other fields including structural ceramics, thermoelectricity, piezoelectricity, etc. However, thorough studies of designing high entropy materials for energy storage and conversion are largely absent. The major obstacle in this field is whether the mixing of multi-component species will necessarily improve targeted properties such as ion transport and chemical absorption/adsorption. In this talk, it will be demonstrated that high entropy can benefit the design of battery cathode materials with high energy density and high rate performance, as well as solid-state ionic conductors that deliver ultra-fast ion conduction. Moreover, future opportunities for designing other high entropy energy materials will also be demonstrated.
3:00 PM Invited
Computational Materials Design and Discovery for Next-generation Solid-state Batteries: Yan Wang1; 1Samsung Semiconductor, Inc.
Computational modeling based on density functional theory has become a cornerstone of materials design, by providing insights into fundamental processes that are not easily accessible in experiments, and enabling fast and efficient prediction even before material synthesis. Such predictive power has made computational modeling a critical tool to design new materials with desired properties and accelerate the development of next-generation batteries. In this talk, we will present recent findings in the physical and chemical design principles for solid-state materials with high ionic conductivity and stability. More specifically, I will discuss crystallographic features which would enable fast ionic transport in inorganic solids, and how high-throughput calculations can be applied with such features in the design and discovery of ionic conductors. I will also discuss our most recent efforts at Samsung, where we are developing tools for inorganic battery materials research by combining high-throughput computation and robotic synthesis machines.
3:30 PM Break
3:50 PM Invited
Millisecond-ion Transport in Mixed Polyanion in Energy Materials: Zeyu Deng1; Tara Mishra1; Eunike Mahayoni2; Jean-Noel Chotard2; Vincent Seznec2; Christian Masquelier2; Gopalakrishnan Sai Gautam3; Pieremanuele Canepa1; 1National University of Singapore; 2Laboratoire de Réactivité et de Chimie des Solides; 3Indian Institute of Science
Mixed polyanion solid electrolytes for solid-state batteries display impressive ionic conductivities. However, the effect of polyanion mixing on ion transport properties is still debated. We will elucidate the role of polyanion mixing on Na-transport properties in Na1+xZr2SixP3-xO12 (0≤x≤3) NASICON electrolytes. Although there is a large body of data on NASICON, transport properties extracted from experiments or theory vary by orders of magnitude, signifying the need to bridge the gap between different studies. More than 2,000 distinct ab initio-based kinetic Monte Carlo simulations serve to map the statistically vast NASICON composition space over an unprecedented time range and spatial resolution and across a range of temperatures. Impedance spectroscopy measurements on NASICONs reveal that the highest ionic conductivity (~ 0.165 S cm-1 at 473 K) is achieved in Na3.4Zr2Si2.4P0.6O12, in line with our predictions (~ 0.170 S cm-1 at 473 K). We show that Si-rich NASICON compositions can achieve high Na+ mobilities.
4:20 PM Invited
Understanding Complex Materials and Interfaces through Molecular Dynamics Simulations: Yifei Mo1; 1University of Maryland, College Park
While first principles computation has achieved great success in materials design and discovery, molecular dynamics (MD) simulations -- both classical and ab initio -- play unique roles in understanding materials mechanisms. MD simulations model the dynamics of atoms with femto-second resolution, which are difficult to directly assess in experiments, and thus can provide unique insights into these complex atomic processes. In this presentation, we will review a number of recent studies from our group, in which we leverage MD simulations for understanding complex phenomena in materials and interfaces, such as super-ionic conductors and solid-electrolyte interfaces. Beyond the understanding of fundamental mechanisms, we will also highlight the design principles one may devise from these simulations.
4:50 PM Invited
Matterverse.ai - A Graph Deep Learning Database of Materials Properties: Shyue Ping Ong1; Chi Chen1; 1University of California-San Diego
The matterverse is vast and complex. It comprises the infinite combinations of elements of the periodic table in ordered and disordered arrangements. In this talk, I will discuss the development of matterverse.ai, a new database and machine learning (ML) prediction platform for materials properties based on graph deep learning. A complement to existing ab initio databases such as the Materials Project, matterverse.ai focuses on probing the matterverse at scales not possible with ab initio methods, for example, predicting properties for millions/billions of hypothetical materials, long-time-scale dynamic simulations, etc. In addition, matterverse.ai will serve as a platform for the sharing of containerized ML models for materials simulations and property predictions. Finally, I will share our vision and future priorities for matterverse.ai, such as leveraging on active learning loops with ab initio databases to continually enhance prediction performance, targeting high-value properties, etc.